Systems for safe and remote outpatient ECG monitoring

ABSTRACT

A system and method providing outpatient ECG monitoring and safe home based cardiac tele-rehabilitation. The system includes a recordation module for recording ECG signals using at least one lead, a tele-rehabilitation module for home based exercise management for a patient&#39;s recovery from a heart disease, the tele-rehabilitation module including a processing module for recognizing erroneous data from the ECG signals and an analysis module for calculating beat-to-beat annotations and determining if an ECG event and/or if a QT interval duration change has occurred. The system can include an exercise module for guiding the patient during an exercise session, a visual display that informs the patient to start and/or to stop the tele-rehabilitation exercise, a visual display and/or audible signal that informs the patient of an incoming or a missed tele-rehabilitation exercise session, and/or a communication module for transmitting/receiving data between the a cardiac tele-rehabilitation module and a physician/monitoring center.

RELATED APPLICATIONS

This application is a Divisional of application Ser. No. 12/090,183filed on Apr. 14, 2008, now issued as U.S. Pat. No. 8,818,496 on Aug.26, 2014, which claims the benefit of U.S. Provisional Application No.60/987,192, filed on Nov. 12, 2007, U.S. Provisional Application No.60/987,180, filed on Nov. 12, 2007, U.S. Provisional Application No.60/987,043, filed on Nov. 10, 2007, U.S. Provisional Application No.60/986,761, filed on Nov. 9, 2007, Polish Patent Application No.P383243, filed on Sep. 2, 2007, entitled “A system for remote cardiacrehabilitation” (English translation), U.S. Provisional Application No.60/948,527, filed on Jul. 9, 2007, and is a continuation ofInternational Application No. PCT/PL2006/000068, filed on Oct. 16, 2006,published in English, which claims priority under 35 U.S.C. §119 or 365to EP Application No. 05077368.8, filed Oct. 14, 2005, the entireteachings of the above applications are incorporated herein byreference.

BACKGROUND

Automated analysis of digitized electrocardiogram (ECG) signals hasvarious applications. Algorithms operating in real-time with the abilityto deal with lead limited signals are useful in external defibrillatorsand lead-limited monitoring systems. They can be also used, as describedhere, in long-term ECG telemetry applications.

In the case of a lead limited ECG, it is difficult to automaticallydistinguish between normal QRS complexes and pathological ECG peaksrepresenting ventricular contractions. Usually pathological complexesare significantly wider and have larger amplitude than normal QRScomplexes; however in some cases the situation may be the opposite. Adecrease in the number of leads causes the number of misinterpretedevents to increase due to a stronger influence of noise and parasiteimpulses in the signal. In a single lead analysis, it is extremelydifficult to distinguish between parasite peaks and QRS complexes due tothe lack of additional leads, which are typically used for reference orcomparison.

SUMMARY

There is provided a system and method providing outpatient ECGmonitoring and safe home based cardiac tele-rehabilitation, even forpatients with high risk of another infarcts. The system includes arecordation module for recording ECG signals using at least one lead, acardiac tele-rehabilitation module for home based exercise managementfor a patient's recovery from a heart disease, the cardiactele-rehabilitation module including a processing module for recognizingerroneous data from the ECG signals and an analysis module forcalculating beat-to-beat annotations and determining if an ECG event hasoccurred and/or if a QT interval duration change has occurred, and acommunication module for reporting the processed ECG signals and/orother detected ECG events including streaming all of the annotations andinformation describing each ECG beat. The system can also include anexercise module for guiding the patient during an exercise session, avisual display that informs the patient to start and/or to stop thetele-rehabilitation exercise, a visual display and/or audible signalthat informs the patient of an incoming or a missed tele-rehabilitationexercise session, and/or a communication module fortransmitting/receiving data between the a cardiac tele-rehabilitationmodule and a physician/monitoring centre.

In some embodiments, the information provided to the patient can includean indication to intensify the exercise because the patient's heart rateis below a predefined threshold, an indication to decrease the exerciseintensity because the patient's heart rate is above a predefinedthreshold, an indication to stop the exercise because a significantcardiac event has occurred, and an indication to contact the patient'sphysician.

In some embodiments, the physician/monitoring centre can include acontrol module for controlling/reprogramming the cardiactele-rehabilitation module. The control module, such as a computer, canallow the patient's physician, or a medical personnel assign to thepatient, to control the patient's tele-rehabilitation exercise session.The control of the tele-rehabilitation exercise session can be based onreviewing received data from the cardiac tele-rehabilitation module, thereceived data including an ECG analysis of the patient and a datadescribing the patient's physical and mental condition. In someembodiments, the data describing patient's physical and mental conditioninclude a blood pressure of the patient, a body weight of the patient, astress information of the patient; a mood information of the patient,and a pharmacotherapy information of the patient. In some embodiments,the data describing patient's physical and mental condition can be inputinto the cardiac tele-rehabilitation module by the patient, by externalmeasuring devices, and/or a person at the physician/monitoring centre.

In some embodiments, the control module can allow for setting ormodifying the tele-rehabilitation program. The settings or modificationscan include a minimum heart rate exercise threshold, a maximum heartrate exercise threshold, a number of exercise sessions during the day, asession duration, a number of exercises during each session, andexercise and pause duration.

In some embodiments, a communication module is used for communicatingthe processed ECG signals and/or other detected ECG event. The detectedECG event can be reported to medical personnel or a wearer of therecordation module. The processed ECG signals can be segmented and thenreported to medical personnel.

In some embodiments, a server can be used for receiving the processedECG signals and/or the ECG event from the reporting module. The servercan receive the processed ECG signals and/or the ECG event from thereporting module for a plurality of patients. The server can direct theprocessed ECG signals and/or the ECG event from the reporting module forthe plurality of patients to medical personnel responsible for arespective patient. The medical personnel can have direct access to apatient's processing module and/or analysis module through the server.The direct access can allow the medical personnel to remotely alterparameters stored in the patient's processing module and/or analysismodule.

In some embodiments, the processing module can include an analysisalgorithm recognizing erroneous data from the ECG signals. The analysisalgorithm can include a noise and distortion detection sub-algorithm(NDDA) for detecting noisy and non-linearly distorted ECG fragmentsincluding detecting distortions produced by not properly attached, tothe patient's body, electrodes. The NDDA can further estimate a broadband noise energy level of the signal.

A method for analyzing limited-lead electrocardiogram (ECG) systemsignals, includes recording ECG signals using at least one lead,performing cardiac tele-rehabilitation, wherein performing cardiactele-rehabilitation includes recognizing erroneous data from the ECGsignals to form a pre-classified ECG signal and determining if an ECGevent has occurred from the pre-classified signal and calculatingannotations for every ECG beat.

The method can further includes reporting the calculated annotations forevery ECG beat and/or the ECG event. The ECG event can be reported tomedical personnel or a wearer of the recordation module. The calculatedannotations representing each ECG beat can be segmented and thenreported to medical personnel.

In some embodiments, the method can further include receiving theannotations for each ECG beat and/or the ECG event at a remote location.The erroneous data from the ECG signals can be detected using ananalysis algorithm. The analysis algorithm can include a noise anddistortion detection sub-algorithm (NDDA) for detecting noisy andnon-linearly distorted ECG fragments. The NDDA can further estimate abroad band noise energy level of the signal and detect distortionsgenerated by not properly attached electrodes. The pre-classified signalcam be analyzed using a beat classification algorithm and/or anarrhythmia detection algorithm. The analyzed signal can be verifiedusing a detection evaluation correction algorithm. The beatclassification algorithm and the arrhythmia detection algorithm cangenerate the calculated annotations for every ECG beat.

In some embodiments, the method can further include updating an averagednormal ECG period for each new non-pathological ECG period based on theperformed beat classification and arrhythmia detection. The averaged ECGperiod can be used for calculating ST segment elevation and for QTinterval duration difference between the averaged ECG period and thereference ECG period. The reference ECG period can be an averaged ECGperiod with a predetermined QT interval, allowing for QT intervaldetermination of each new averaged ECG period based on the predeterminedinterval value and the current QT interval difference value. The QTinterval difference is obtained by finding a best match of a time domainshifted T wave representation signal of the averaged ECG period and theT wave representation signal of the reference ECG period. The signalrepresentations can be difference signals of the averaged ECG period andthe reference ECG period. The best match of the time domain shifted Twave representation of the averaged ECG period and the T waverepresentation of the reference ECG period can be the maximum value of ashifted T wave matching function. The index of the maximum value of asimilarity function can be interpolated to enhance the T wave shiftaccuracy. The interpolation can be performed by a parabola fitting tothe maximum similarity value and a surrounding values of the similaritycurve. The maximum similarity value can be the maximum of all maximumsimilarity values of the reference ECG period and all collectedauxiliary reference ECG periods compared with a new averaged ECG period.The auxiliary reference ECG periods can be collected if a shape changingT wave occurs. The averaged ECG period collected can be used as anauxiliary reference ECG period.

In some embodiments, a non-pathological ECG period can be a current ECGperiod used for calculating a T wave alternans amplitude. A value of thebase line level can be a median value of an isoelectric line signalsegment preceding a current ECG period. The value of a base line leveldeviation can be a standard deviation of the current base line level anda J preceding base line level. The value of the isoelectric linedeformation of the current ECG period can be a standard deviation of adifference of the current isoelectric line, preceding the current ECGperiod and an isoelectric line preceding the previous ECG period.

In some embodiments, an unbiased current ECG period can be calculated byremoving a low frequency T wave shape trend from the current ECG period.The low frequency T trend can be removed by subtracting an averaged ECGperiod from the current ECG period.

In some embodiments, a periodicity values representing each sample ofthe current ECG period can be calculated. The periodicity values for nsamples of the current ECG period can be calculated based on nautocorrelation sequences, calculated with the use of a J consecutiveunbiased ECG periods, including the current unbiased ECG period. A Twave amplitude can be calculated based on anunbiased-averaged-difference-ECG-period. Theunbiased-averaged-difference-ECG-period can be calculated based on J/2pairs of a J consecutive unbiased ECG periods, the preceding the currentunbiased ECG period and including the current unbiased ECG period. The Twave alternans can be a maximum of theunbiased-averaged-difference-ECG-period. Anunbiased-averaged-difference-ECG-period can be weighted by theperiodicity values. A maximum value of theunbiased-averaged-difference-ECG-period, weighted by periodicity valuesand compensated by base line drift deviation values and isoelectric linedeformation values can be a calculated T wave alternans amplitude forthe current ECG period.

The system and methods provide a real-time and long-term outpatient ECGmonitoring system with real-time and remote access to the monitoringresults. The methods and systems allow for decentralized, in terms ofpatient location, QT interval monitoring studies with remote access tothe ECG analysis devices and analysis results. Remote access to thereal-time data allows for controlling the study from any location.Real-time access to the monitoring devices, and the methodology allowsfor controlling and correcting the QT/QTc interval measurements in veryefficient manner, and allows for processing large amounts of data(long-term monitoring periods, many patients at the same time)simultaneously by a single operator.

Analysis of a lead-limited ECG is extremely difficult because thesignals contain a large number of ambiguities, i.e., signals fromvarious patients may be very substantially different. When disturbancesoccur in these signals, it is easy to confuse parasite impulses or peakswith the impulses generated by the heart. In addition, such analysis, bydefinition, provides limited amount of information in comparison to atypical 12 lead ECG.

The present systems and methods provide for lead-limited ECG signalanalysis having the ability to automatically detect QRS complexes,classify the detected beats, identify heart arrhythmias and ST segmentelevation events. Identifying these elements is necessary to performrobust and efficient automated QT interval measurements. Real-timeprocessing is important in case of long-term monitoring intervals, whereit is necessary to instantly access the QT interval changes.

In addition, based on a real-time analysis produced by the devicecarried by the patient, the systems can be used for remote hometele-rehabilitation, where intensity of the exercised performed by thepatient is controlled by the device, based on a calculated in real-timeheart rate. Since the system sends the analysis results all the time (24hours a day) also including the rehabilitation session, the heartcondition of the patient is constantly monitored and therefore therehabilitation process is safe. In case of detecting abnormalities inthe ECG signal in real-time the patient is informed to stop the exerciseand to contact a medical personnel. As described above, the systemprovides possibility of constant, beat-to-beat monitoring of thepatient's ECG, where automatically selected ECG strips plus annotationsof every beat are transmitted to the physician responsible forcontrolling the rehabilitation progress.

Constant monitoring during the day and during the night is ofimportance, because certain ECG based predictors allow to determinepossibility of potential future problems. The predictors are presence ofcertain arrhythmias, ST segment elevation changes, QT/QTc intervalchanges, T wave alternans amplitude. The system allows for detection ofall of the mentioned parameters in real-time. Moreover, the patient,before starting the exercise session is obliged to provide informationdescribing his/her physical and mental condition. This data, along withthe automatically detected ECG findings is remotely transmitted to themonitoring centre for analysis. Only in case of positive evaluation ofthe mentioned data, the patient is allowed to exercise. The exerciseguiding software is remotely triggered by the physician from themonitoring centre.

Analysis of a lead-limited ECG is a difficult task, because the signalscontain a large number of ambiguities, i.e., signals from variouspatients may be very substantially different. When disturbances occur inthese signals, it is easy to confuse parasite impulses or peaks with theimpulses generated by the heart. In addition, such analysis, bydefinition, provides limited amount of information in comparison to atypical 12 lead ECG. Therefore, the lead-limited ECG analysis presents agreater challenge from an algorithmic point of view.

The present systems and methods provide for lead-limited ECG signalanalysis having the ability to automatically detect QRS complexes,classify the detected beats, identify heart arrhythmias, calculate STsegment elevation, calculate QT interval duration and T wave alternans(TWA) amplitude. Identifying these elements is useful in determining thepatient's heart condition and useful in predicting potential problems.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages will beapparent from the following more particular description of embodiments,as illustrated in the accompanying drawings in which like referencecharacters refer to the same parts throughout the different views. Thedrawings are not necessarily to scale, emphasis instead being placedupon illustrating the principles of the embodiments.

FIG. 1 is an illustration of a waveform output from an electrocardiogram(ECG) (for a single cardiac cycle);

FIG. 2 is an illustration of a system diagram for monitoring patientsfrom remote locations;

FIG. 3 is a schematic illustration of the system of FIG. 2;

FIG. 4 is a schematic illustration of a device for performing alead-limited ECG signal analysis;

FIG. 5 is a flow-chart of an embodiment of a lead-limited ECG signalanalysis algorithm;

FIG. 6 is a graph of an input signal (upper) and preprocessed signalwith regard to Equations 4-7 (lower);

FIG. 7 is a graph of local extremes and T wave shape calculation points;

FIG. 8 is a graph of a clean ECG spectrum (upper) and anUnpredictability Measure (UM) graph (lower);

FIG. 9 is a graph of a noisy ECG spectrum (upper) and a UM graph(lower);

FIG. 10 is a graph of an averaged ECG period used for ST and QTcalculations;

FIG. 11A shows a new T wave (dotted) and a reference T wave (solid) inupper part of the figure and the bottom part of the figure presentsdifference signals of the new T wave (dotted) and difference signal ofthe reference T wave (solid);

FIG. 11B is a similarity curve of a time domain shifted differencesignals of the example T waves from FIG. 11A;

FIG. 11C presents six (6) consecutive ECG periods with TWA present andwith a slow T wave shape evolution during a ST elevation event in six(6) consecutive ECG periods;

FIG. 11D presents six (6) consecutive unbiased ECG periods with TWApresent and with slow T wave shape evolution during ST elevation eventin six (6) consecutive ECG periods; and

FIG. 11E is an periodicity curve (upper) and anunbiased-averaged-difference-ECG-period (bottom).

DESCRIPTION

In general, there is provided a real-time and long-term outpatient ECGmonitoring system with real-time and remote access for the monitoringresults. On one hand the system can be viewed as a remote real-timeholter system, where an analysis report, very much similar to the holterreport, can be generated, but on the other hand the system works inreal-time and transmits the analysis results from the patient, over anetwork, such as the internet and/or a mobile telecommunication network,to physician, or monitoring personnel who can immediately access thedata.

An electrocardiogram (ECG) is a graphical display produced by anelectrocardiograph, which records the electrical activity of the heartover time. The graphical display is a series of electrical phenomenaresulting from a trial and ventricular depolarization and repolarizationof the heart muscle.

FIG. 1 represents single cardiac cycle (single heart beat), with adescription of ECG peaks, waves and an intervals, which are the basis ofECG analysis and classification. A typical ECG tracing 10 of a normalheartbeat (or cardiac cycle) consists of a P wave 12, a QRS complex 14and a T wave 16. A small U wave (not shown) is normally visible in 50 to75% of ECGs. The baseline voltage of the electrocardiogram is known asan isoelectric line. Typically, the isoelectric line is measured as theportion of the tracing following the T wave 16 and preceding the next Pwave 12.

The P Wave 12 is seen during normal a trial depolarization, a meanelectrical vector is directed from the SA node towards the AV node, andspreads from the right atrium to the left atrium. The relationshipbetween P waves 12 and QRS complexes 14 helps distinguish variouscardiac arrhythmias. For example, the shape and duration of the P waves12 may indicate a trial enlargement.

The PR segment (interval) 18 is measured from the beginning of the Pwave 12 to the beginning of the QRS complex 14. The PR interval 18 isusually 120 to 200 ms long. On an ECG tracing, this corresponds to 3 to5 small boxes. A prolonged PR interval 18 may indicate a first degreeheart block; a short PR interval 18 may indicate a pre-excitationsyndrome via an accessory pathway that leads to early activation of theventricles, such as seen in Wolff-Parkinson-White syndrome; a variablePR interval 18 may indicate other types of heart block; a PR interval 18depression may indicate a trial injury or pericarditis; and variablemorphologies of P waves 18 in a single ECG lead is suggestive of anectopic pacemaker rhythm, such as wandering pacemaker or multifocal atrial tachycardia.

The QRS complex 14 is a structure on the ECG that corresponds to thedepolarization of the ventricles. Because the ventricles contain moremuscle mass than the atria, the QRS complex 14 is larger than the P wave12. In addition, because the His-Purkinje system coordinates thedepolarization of the ventricles, the QRS complex 14 tends to look“spiked” rather than rounded due to the increase in conduction velocity.A normal QRS complex 14 is 0.06 to 0.10 sec (60 to 100 ms) in duration.The duration, amplitude, and morphology of the QRS complex 14 is usefulin diagnosing cardiac arrhythmias, conduction abnormalities, ventricularhypertrophy, myocardial infarction, electrolyte derangements, and otherdisease states. Q waves 22 can be normal (physiological) orpathological.

The ST segment 20 connects the QRS complex 14 and the T wave 16 and hasa duration of 0.08 to 0.12 sec (80 to 120 ms). The ST segment 20 startsat a J point (junction between the QRS complex 14 and ST segment 20) andends at the beginning of the T wave 16. However, since it is usuallydifficult to determine exactly where the ST segment 20 ends and the Twave 16 begins, the relationship between the ST segment 20 and T wave 16are typically examined together. The typical ST segment 20 duration isusually around 0.08 sec (80 ms). It should be essentially level with thePR segment 18 and TP segment (not shown). A normal ST segment 20 has aslight upward concavity. Flat, downsloping, or depressed ST segments 20may indicate coronary ischemia, while elevated ST segment 20 mayindicate myocardial infarction.

The T wave 16 represents the repolarization (or recovery) of theventricles. The interval from the beginning of the QRS complex 14 to theapex of the T wave 16 is referred to as the absolute refractory period.The last half of the T wave 16 is referred to as the relative refractoryperiod (or vulnerable period). Inverted (or negative) T waves 16 can bea sign of coronary ischemia, Wellens' syndrome, left ventricularhypertrophy, or CNS disorder. Tall or “tented” symmetrical T waves 16may indicate hyperkalemia. Flat T waves 16 may indicate coronaryischemia or hypokalemia.

The QT interval 24 is measured from the beginning of the QRS complex 14to the end of the T wave 16. A normal QT interval 24 is usually about0.40 seconds. The QT interval 24 as well as the corrected QT interval 24are important in the diagnosis of long QT syndrome and short QT syndromeand also are important in ventricular tachyarrhythmia prediction.

The U wave is typically small, and not always seen, and by definition,follows the T wave 16. U waves are thought to represent repolarizationof the papillary muscles or Purkinje fibers. Prominent U waves are mostoften seen in hypokalemia, but may be present in hypercalcemia,thyrotoxicosis, or exposure to digitalis, epinephrine, and Class 1A andClass 3 antiarrhythmics, as well as in congenital long QT syndrome andin the setting of intracranial hemorrhage. An inverted U wave mayrepresent myocardial ischemia or left ventricular volume overload.

T-wave alternans (TWA) is a non-invasive test of the heart that is usedto identify patients who are at increased risk of sudden cardiac death.It is most often used in patients who have had myocardial infarctions(heart attacks) or other heart damage to see if they are at high risk ofdeveloping a potentially lethal cardial arrhythmia. Those patient's whoare found to be at high risk would therefore benefit from the placementof a defibrillator device which can stop an arrhythmia and save thepatient's life.

The TWA test uses an electrocardiogram (ECG) measurement of the heart'selectrical conduction. The test looks for the presence of repolarizationalternans (T-wave alternans), which is a variation in the vector andamplitude of the T-wave component of the ECG. The amount of variation issmall, on the order of microvolts, so sensitive digital signalprocessing techniques are required to detect TWA.

TWA were first described in 1908, and at that time when sensitivedigital techniques were not available, only large variation(“macroscopic” TWA) could be detected. Those large TWAs were associatedwith increased susceptibility to lethal ventricular tachyarrhythmias.

FIG. 2 is an illustration of the system 100 diagram for provide remoteoutpatient cardiac tele-rehabilitation and a long-term ECG monitoring.The system 100 also provides real-time and automated QRS detection, beatclassification, ECG arrhythmia detection, ST segment elevationmonitoring, QT interval duration monitoring and TWA amplitudemonitoring. Monitoring of these elements is necessary for remotetele-rehabilitation control and allows for predicting potential heartfunctionality problems. The system 100 includes, but is not limited too,a recordation module 110, a cardiac tele-rehabilitation module 120, anddata transmission network 130, and a remote monitoring module 140.

The recordation module 110 can be an ECG digitization and wirelesstransmission device, such as a Bluetooth device, for recording ECGsignals using at least one lead (112, 114). The recordation device 110operates on signal from a first electrode 112 and a second electrode 114(single channel or single lead) or three or more electrodes (multichannel or multi-lead).

The tele-rehabilitation module 120 provides real-time ECG monitoringwhile recognizing erroneous signals from the ECG signals; real-timecontrol of tele-rehabilitation exercise duration; pause insertion toseparate exercise duration; exercise intensity; and a two-way datacommunication channel between a patient and a physician. Prior to system100 operation, the patient inputs patient information, not discernableby the system 100, into the tele-rehabilitation module 120. The patientinformation can include patient body weight, blood pressure, patientmood data, and patient pharmacotherapy related information.

In operation, the tele-rehabilitation module 120, in conjunction withthe recordation module 110, acquires ECG analysis related informationand guides the patient though an exercise or series of exercises. Thedata received by the tele-rehabilitation module 120 is analyzed todetermine the patient state. For example, if the tele-rehabilitationmodule 120 detects a heart rate above or below a predefined threshold,it informs the patient to intensify or the exercise. If the patientstate is normal, the tele-rehabilitation module 120 indicates thepatient can continue the tele-rehabilitation exercise/program. If apotential threat is detected, the tele-rehabilitation module 120indicates the patient to stop the tele-rehabilitation exercise/programand/or contact the monitoring centre 140 for further instructions. Thetele-rehabilitation module 120 also forwards all the gatheredinformation to the physician/monitoring centre 140 through the datatransmission network 130. The gathered information includes patient bodyweight, patient stress data, patient mood data, and patientpharmacotherapy related information, blood pressure, and the ECGanalysis related information.

The remote monitoring module or physician/monitoring centre 140 caninclude a desktop computer 142, having various computer peripherydevices, such as a monitor 144, a keyboard 146, and a mouse 148. Theremote monitoring module 130 can include software for ECG visualization;editing/configuring the remote ECG analysis algorithm configuration;editing/configuring the remote tele-rehabilitation exercise/program; andcontrolling the tele-rehabilitation progress.

The data transmission network 130 can include a mobile telephone network132, internet backbone 134, computer servers 136, or a combinationthereof, or like devices, to facilitate data transmission between thetele-rehabilitation module 120 and the remote monitoring module 140. Theservers 146 can include software for data management and communicationbetween the tele-rehabilitation module 120 and the remote monitoringmodule 140. It should be understood any type of data transmissionnetwork can be used to facilitate the data transmission between thetele-rehabilitation module 120 and the remote monitoring module 140.

FIG. 3 represents a schematic representation 200 of the system 100 ofFIG. 2. The system 100 includes the recordation module 110, the cardiactele-rehabilitation module 120, and the data transmission network 130,and the remote monitoring module 140.

The recordation module 110 can be an ECG digitization and transmissionunit operable on two (2) electrodes (single channel), or three (3)electrodes (multi channel).

The tele-rehabilitation module 120 can include a processing module 121,an analysis module 122, an exercise module 123, a visual display 124, anaudible device 125, and a communication/reporting module 126.

In some embodiments, the tele-rehabilitation module 120 can be a touchscreen type device, such as a personal digital assistant (PDA) orSmartPhone. The processing module 121 can be a microcomputer, processor,or any other known type device. It should be understood that theprocessing module 121 includes a memory storage device. The analysismodule 122 can be software operable on processing module 121. Theexercise module 123 can be further software operable on processingmodule 121, or part of, or a sub-component of, the analysis module 122,or any combination thereof. The visual display 124 can be a type-screentype display, or any other known type device. The audible device 125 canbe a speaker, or any other known type device. Thecommunication/reporting module 126 can be a GSM phone module, a CMDAphone module, a TDMA phone module, or the like fortransmitting/receiving data information between the cardiactele-rehabilitation module 120 and the remote monitoring module 140.

The system 100 manages a tele-rehabilitation program for a patient andmonitors an ECG signal in real-time, at any location of the patient. Inaddition, once the system 100 detects a pathological cardiac event itautomatically performs a predefined action. These predefined actions caninclude: (1) raise a sound alarm on the PDA, i.e., to stop the exercise,or to wake up the patient, etc.; (2) send a relevant ECG signal fragmentwith an analysis report to a server for further analysis; (3) send adiagnosis confirmation; (4) archive the event; and/or (5) store the ECGfragment and analysis report on the PDA memory card for later reference.

The system 100 provides ECG detecting arrhythmias and ST segmentelevation monitoring, QT segment duration monitoring and TWA amplitudemonitoring with automatic analysis and pre-selection of pathologicalevents. The system 100 selects and immediately sends these ECG tracingintervals where an abnormal event took place. The system 100 is alsoeffective during the night, when the patient does not feel any symptoms,or during daytime asymptomatic (but potentially important) events, whichmay occur during the tele-rehabilitation exercises, or between theexercise sessions.

The system 100 allows for simultaneous monitoring and controlling of thetele-rehabilitation progress of larger number of patients at the sametime, due to effective automated events detection in real-time. It isalso possible to configure the system 100 to periodically send an ECGsegment, even if no pathologies are detected, or to provide ECG ondemand by a requesting physician (any ECG fragment can be requested by adoctor).

The microcomputer based software running on the PDA 120 is responsiblefor managing wireless communication with the ECGtransmission/acquisition unit 126; managing ECG analysis data exchangevia mobile telephone network 132 with the server 136 application(directly) and with the desktop 142 based client application(indirectly—through the server application); initializing patientexamination; initializing tele-rehabilitation exercise sessions, guidingthe patient through the exercises and providing heart rate informationin real-time to allow the patient to control the exercise intensity;introducing patient condition related data before eachtele-rehabilitation exercise session; managing patient condition relateddata exchange via mobile telephone network 132 with the server 136application (directly) and with the desktop 142 based client application(indirectly—through the server application); storing and managing ECGdata; visualizing, in real-time, the ECG waveform, annotations, STsegment elevation, QT interval, TWA amplitude and other parameters;viewing and analyzing recorded ECG signals; generating analysis reports;displaying text and graphical messages, which describe the examinationstatus; examination time; microcomputer battery status; ECGacquisition/transmission unit battery status; wireless connectionstatus; text messages obtained from the server (i.e., messages from aphysician); other information related to the examination; configuringthe ECG analysis algorithms; configuring events and actions; andconfiguring tele-rehabilitation program, enabling the patient tomanually send the current ECG fragment to the server (i.e., and to thephysician's desktop computer).

The desktop based software running on the desktop 142 is responsible fordirect synchronization (via e.g., USB cable) with the microcomputer 121to initialize the examination and link the patient's personal data withthe examination ID generated by the microcomputer software; set/edit theexamination settings (e.g., tele-rehabilitation program, ECG analysissettings, events settings and action settings); finalize the examinationand link the patient personal data with the examination ID (in case ofremotely initialized examinations); HL7 data editing and management;visualization of ECG signals with annotations obtained from the server(i.e., the signals and annotations are submitted to the server, duringthe examination by the microcomputer in real-time); present the patientcondition describing information, submitted by the patient before eachexercise session; remote triggering of the exercise guiding softwarerunning on the patient's PDA 120; remote configuration of theexamination settings on the PDA 120 (through the server application);sending text and/or graphical messages to the PDA 120 screen display(via the server application), for instance to inform the patient to takea specific medication, to modify the medication dose, or to check theelectrodes, which may not be well attached to the patient's body, etc.;inform the user (e.g., physician) of incoming ECG fragments from themonitored patients; manage the recordings (examinations), such asadding; removing; editing; report generation; ECG and annotationvisualization; events based navigation through the entire examination;report based navigation through the entire examination; and navigatingthrough the entire examination based on trends and auxiliaryinformation.

In some embodiments, server software is responsible for data exchangebetween the PDA 120 carried by the patient (responsible for managing thetele-rehabilitation and for analyzing the ECG in real-time,beat-by-beat) and the desktop computer or control module 142 used by thedoctor/physician/operator (for reviewing the analysis results and remoteprogramming the monitoring analysis algorithms and thetele-rehabilitation program and controlling the tele-rehabilitationprogress and patient condition, for viewing the examination progress,the incoming ECG fragments, annotations describing detected arrhythmias,beat classification, QT interval duration, ST segment elevation, TWAamplitude and other auxiliary information, for editing the results,approving the diagnosis, etc.). The server application gathers datasubmitted by the microcomputer 121 (via the mobile telephone network132) and stores it in a database for the physician to access fromhis/her desktop 142. The data may contain event description, eventpriority, event types, etc. Similarly, all information provided by thephysician's desktop application for the PDA microcomputer 121(patient's) application is managed by the server application.

The communication procedure, i.e., communication between the PDAmicrocomputer 121 application, responsible for constant monitoring andanalysis of the patient's ECG and the desktop visualization/editingapplication operated by the physician on the desktop computer 142, hasbeen designed to allow the doctor to efficiently monitor a large groupof patients at the same time (Communication scheme of FIG. 1). Thiseffect is achieved by pre-selection (filtering) and auto-analysis of theinformation before it is presented to the specialist. The filtering andanalysis is performed using ECG analysis algorithms operating on thepatient's PDA 120 or microcomputer 121. Embedding the ECG analysisalgorithms on the microcomputer has additional benefits. In case oflimited (insufficient) bandwidth of the mobile telecommunicationnetwork, unnecessary data (i.e., normal ECG signal) is not send to thephysician or monitoring specialist.

It is important to note that all information describing every ECG beat,such as TWA amplitude (for each lead); QT interval; similarity of each Twave to the reference T wave used for QT interval changes calculations;beat type; QRS location in time; arrhythmia type; ST segment elevation(for each lead); similarity to averaged PQRST complex; ADC networkinterference level (for each lead); and broad band noise level (for eachlead) are transmitted to the server (and are available at the desktopapplication). Such a set of parameters has been carefully chosen toenable, even without viewing the accompanying ECG waveform,discriminating between clean ECG and misclassified artifacts. In case ofdoubts, the desktop application interface allows for requesting any ECGfragment. This allows the physician access to any ECG fragment stored inthe microcomputer 121 memory at any time (such a request message issubmitted to the microcomputer 121 application via the serverapplication, and the requested ECG fragment is immediately returned tothe server for further analysis.

This set of parameters, also allows for very quick navigation throughvery long ECG recordings, and for simultaneous management of manypatients by one trained specialist. The information set is alsosufficient to generate daily reports, generate ECG analysis statistics,etc.

Another important feature of the communication procedure is theinteractivity of the system, available during the examination. Thephysician, using of his/her desktop 130 application can re-configuretele-rehabilitation program, re-configure the ECG analysis algorithm (ifthe algorithm is too sensitive to artifacts, or not sensitive enough toslightly varying ECG multi-forms, etc), re-configure event settings,action settings and other settings of the microcomputer application,request any ECG fragment (as explained earlier), or send text andgraphical messages to the patient (to e.g., change medications, toreplace electrodes, or to request contact with the monitoring center,etc.) at any time. Moreover, the doctor may manually correct algorithmfindings (manually supervise algorithm training with the desktopapplication, based on the received data) and remotely provide theMicrocomputer, running the analysis algorithms, with the trainingadjustments. In another example, the patient may decide to manually sendECG fragment to the doctor (perceived symptom based event), or request atelephone contact by simply taping an appropriate button on themicrocomputer touch-screen.

FIG. 4 schematically illustrates an embodiment of the processing module121 for the lead-limited ECG signal analysis. The processing module 121includes a DC removal unit 210, a signal pre-processing unit 220, adetection and evaluation unit 230, an arrhythmia detection unit 240, anda final processing unit 250. In some embodiments, these units (210-250)may be facilitated in hardware of the processing module 121 or softwareof the analysis module 122.

In operation, the ECG signal “S” obtained from a patient is input intothe processing module 121. From the DC removal unit 210, the signalenters into the signal pre-processing unit 220 responsible forperforming the signal pre-processing, QRS detection and beatclassification. From the signal pre-processing unit 220, the signal isprovided to the arrhythmia detection unit 240, that is responsible forarrhythmia detection according to predefined logic rules utilizingexpert systems. In particular, the expert systems may employ neuralnetworks, fuzzy logic, statistical methods, etc. At the same time, thesignal from the DC removal unit 210 is provided to the detection andevaluation unit 230, where detection evaluation, noise and distortiondetection, as well as auxiliary information calculation is determined.Signals from the detection and evaluation unit 230 and the arrhythmiadetection unit 240 are then fed to the final processing unit 250, whichincludes preparation of information “V” in a user-readable format.

FIG. 5 a flow-chart 300 of an embodiment of a lead-limited ECG signalanalysis algorithm, where a signal S[n][k], k-representing number ofleads is subject to the number of processing steps. The algorithmincludes the following functional sub-algorithms or blocks: a signalpre-processing algorithm (SPA) 310; a QRS detection algorithm (IQDA)320; a beat classification algorithm (BCA) 330; an arrhythmia detectionalgorithm (ADA) 340; a detection evaluation algorithm (DEA) 350; a noiseand distortion detection algorithm (NDDA) 360; and an auxiliaryinformation calculation algorithm (AICA) 370.

The Signal Pre-processing Algorithm (SPA) 310 is responsible for ACinterference elimination which is performed by a digital IIR notchfilter. The module also removes DC offset and ultra low frequencybaseline drift, utilizing delay free linear phase filtering routine,originally developed for the purpose of this application. The routinecan be described in the following steps: signal is divided intooverlapping blocks; block sizes are related to the averaged heart-rate(HR); mean value of each block is calculated and subtracted for thatblock; and the ECG signal is reconstructed by cross-fading thesuccessive segments.

Eq. 1 presents on embodiment of a base line correction algorithm for pthECG segment:

$\begin{matrix}{{y_{p}\lbrack l\rbrack} = {{s_{p}\lbrack l\rbrack} - \frac{\sum\limits_{k = {l - {L_{p}/2}}}^{l + {L_{p}/2}}\;{s_{p}\lbrack k\rbrack}}{L_{p}}}} & (1)\end{matrix}$where:sp—input signal segment;yp—output signal block;p—segment index;l, k—sample indices; andLp—pth block size (HR dependent).

The output ECG is constructed from overlapping blocks weighted by across-fading window:

$\begin{matrix}{{y\lbrack n\rbrack} = {\sum\limits_{p = 1}^{P}\;{{y_{p}\lbrack l\rbrack} \cdot {w\lbrack l\rbrack}}}} & (2)\end{matrix}$where:y—output signal;yp—output signal in pth block;n, l—sample indices; andL, N—block size and signal length consequently.w—cross fading window, e.g., any suitable function of the generalizedcosine windows:

$\begin{matrix}{{w\lbrack n\rbrack} = {A - {B \cdot {\cos( {( {n - 1} ) \cdot 2 \cdot \frac{\pi}{n - 1}} )}} + {C \cdot {\cos( {2 \cdot ( {n - 1} ) \cdot 2 \cdot \frac{\pi}{n - 1}} )}}}} & (3)\end{matrix}$where:N—window size; andA, B, C—definable constants.

The presented approach is computationally effective, does not disturbphase of the processed signal and does not introduce oscillatorydistortions (so called Gibb's effect) to the processed signal.

Further, QRS detection is performed by statistics based methods in anIntelligent QRS Detection Algorithm (IQDA) 320, which is utilizinginformation about average HR, higher order statistics description of therhythm evolution, QRS complexes properties, such as shape and amplitude,T wave shape, base line behavior, noise level and many others, providingvery robust decision results. The non-linear prediction of evolving HR(calculated for each beat) allows for calculating the expected QRStime-domain position. This information combined with peak shape analysisand peak level versus surrounding noise level analysis allow for robustQRS complexes detection. Peaks shape analysis and noise level estimationare further described in the BCA block 330 description and the NDDAblock 360 description.

Next, a Beat Classification Algorithm (BCA) 330 is used. Based onfeature vector (FV) containing description of currently analyzed beat,statistical classifier and ANN system (Artificial Neural Network)performs classification with accordance to peak shape, width, amplitude,T-wave shape, and with reference to previously calculated featurevectors representing reference normal beats, reference pathologicalbeats and as well as recently classified beats. After primaryrecognition, BCA performance is evaluated with reference tomultidimensional prediction coefficients, describing evolution of thefeature vectors. Based on the Euclidean distance between predicted FVand measured FV, unpredictability measure parameter for the current beatis estimated and primary recognition improved. The peak shapedescription parameters are obtained from a preprocessed ECG block. Thepreprocessing routine is based on a linear operations utilizing movingaverage filter bank and difference function. In one embodiment, thepreprocessing is performed in the following steps:

$\begin{matrix}{{v_{1}\lbrack n\rbrack} = {\frac{1}{K}{\sum\limits_{k = {n - {K/2}}}^{n + {K/2}}\;{y\lbrack k\rbrack}}}} & (4) \\{{v_{2}\lbrack n\rbrack} = {\frac{1}{l}{\sum\limits_{k = {n - {L/2}}}^{n + {L/2}}{v_{1}\lbrack {N - l + 1} \rbrack}}}} & (5) \\{{v_{3}\lbrack n\rbrack} = {\frac{1}{l}{\sum\limits_{k = {n - {L/2}}}^{n + {L/2}}{v_{2}\lbrack {N - l + 1} \rbrack}}}} & (6) \\{{{y^{\prime}\lbrack n\rbrack} = {{v_{3}\lbrack n\rbrack} - {v_{1}\lbrack n\rbrack}}}{{where}\text{:}}{{N - {{signal}\mspace{14mu}{length}}};}{{K = \lfloor \frac{fs}{25} \rfloor};}{{L = \lfloor \frac{fs}{50} \rfloor};}{and}{{fs} - {{sampling}\mspace{14mu}{{rate}.}}}} & (7)\end{matrix}$

FIG. 6 presents the input (upper part of the figure) and thepreprocessed signals with regard to Equations 4-7. Difference ratios ofthe local extremes, marked by circles, related to each QRS complex areused in the FVs as peak shape descriptors.

The peak width (also used in FVs) is obtained by calculating adifference between the left and the right index of the local extremesfor each beat (left and right peak marked by circle, for each QRScomplex consequently, e.g., x3−x1 in FIG. 7). In addition, auxiliarywidth calculation algorithm, operating in the frequency domain, is used.In the first step of this method derivative of a smoothed ECG (v1 fromEq. 4) is calculated, i.e.:dv ₁ [n]=v ₁ [n+1]−v ₁ [n]  (8)

Then, 400 ms regions surrounding each QRS complex are transformed intofrequency domain with the use of FFT algorithm. In practice, the numberof samples representing the analyzed region is rounded to the nearestpower of two for computational efficiency, i.e.:N=2^(round(log) ² ^((0.4·fs)))  (9)where:fs—sampling rate,N—FFT order,round( . . . )—rounding to the nearest integer towards zero.

The spectrum based width is obtained in the following way:

$\begin{matrix}{w = {2 \cdot {( {\frac{N}{2} + 1} )/( {\frac{\sum\limits_{n = 1}^{N/2}\;{{{X\lbrack n\rbrack}} \cdot n}}{\sum\limits_{n = 1}^{N/2}\;{{X\lbrack n\rbrack}}} - 1} )}}} & (10)\end{matrix}$were:w—spectrum based QRS complex width,N—FFT size (analyzed region size),X—complex spectrum of the derivative of a smoothed ECG (dv1).

The parameters describing T wave shape (also utilized in the FeatureVectors), are obtained with regard to the local extremes time position(See FIG. 4 and also x1, x2, x3 in FIG. 7).

T1, T2 and T3 points are calculated in the following way:

T1=x3;

T2=x3+(x3−x2); and

T3=x3+2*(x3−x2).

Values representing T wave shape are base line compensated ECG valuesfor T1, T2 and T3 time instances. The baseline compensation is obtainedby subtracting median value of the ECG fragment preceding by 160 ms thex1 point.

It must be stressed that each detected QRS complex is described withsuch a set of parameters and based on neighbor QRS complexes similarity,measured with an Euclidean distance of the FVs and rhythm properties(considering only non-premature beats) reference normal FVs areobtained, and kept in the N set. All calculated FVs, during thereal-time processing are compared to FVs from N set and classified asnormal (if similar to any FV from N) or potentially pathological orunknown beats. The potentially pathological or unknown beat FVs are keptfor further reference in the P set. In case of newly calculated FV notsimilar to the reference FVs from N, but similar to one, or more FVsfrom P, it is classified as pathological. Otherwise (if not similar toany FV from P nor N) it is classified as unknown. Since the P and N setsare constantly updated, the algorithm has ability to learn and adopt tochanging conditions.

Further, an Arrhythmia Detection Algorithm (ADA) 340 is utilized. Afterbeats classification, ADA 340 performs arrhythmia detection withaccordance with the predefined logic rules as shown below, howeversimilarly to BCA 330 primary recognition process, the decision makingmodule in ADA 340 is based on statistical classifier which adaptively(to the rhythm behavior) performs classification task. The ADA 340algorithm monitors HR changes, and in case of rapid heart rate increases(assuming sufficiently high BPM) recognizes tachycardia rhythms. In caseof chaotic rhythm, assuming sufficient similarity of consecutive beats,a trial fibrillation is detected.

Next, a Detection Evaluation Algorithm (DEA) 350 is employed. Beatrecognition and arrhythmia detection is performed for all fragments(even extremely distorted) of the ECG signal. In some cases, however,distortions and noise level may cause misclassifications. Based on thebase line drift fluctuations, calculated with the use of 160 ms ECGfragments preceding x1 point for each QRS complex (see FIG. 7),distortions are detected. Also noise level, non-linear distortiondisturbances (hard clipping) and neighbor similarity of consecutive QRScomplexes (calculated with regard to FVs used in BCA 330) are used as asignal condition descriptor.

At the same time, a Noise and Distortion Detection Algorithm (NDDA) 360is used for detecting non-linearly distorted ECG fragments, usuallycaused by hard clipping. The detector analyzes consecutive samples, andin case of identical values over and under predefined maximum andminimum threshold, detects the distortion. In addition a broad bandnoise energy is estimated with the use of Unpredictability Measure (UM)algorithm. The UM algorithm is very much suitable for ECG analysis,because the electrocardiographic signal is quasi-periodic (similarly toaudio signals) and chaotic phase of the spectrum components is relatedto parasite noise. UM is calculated in the following way:

$\begin{matrix}{\alpha_{k}^{t} = \frac{\sqrt{( {{{r_{k}^{t} \cdot \cos}\;\Phi_{k}^{t}} - {{{\hat{r}}_{k}^{t} \cdot \cos}\;{\hat{\Phi}}_{k}^{t}}} )^{2} + ( {{{r_{k}^{t} \cdot \sin}\;\Phi_{k}^{t}} - {{{\hat{r}}_{k}^{t} \cdot \sin}\;{\hat{\Phi}}_{k}^{t}}} )^{2}}}{r_{k}^{t} - {{\hat{r}}_{k}^{t}}}} & (11)\end{matrix}$For r_(k) ^(t) denoting spectral magnitude and Φ_(k) ^(t) denotingphase, both at time t, while {circumflex over (r)}_(k) ^(t) and Φ_(k)^(t) represent the predicted values of Φ_(k) ^(t), and are referred tothe past information (calculated for two previous signal sample frames):

$\begin{matrix}{\alpha_{k}^{t} = \{ \begin{matrix}{{\hat{r}}_{k}^{t} = {r_{k}^{t - 1} + ( {r_{k}^{t - 1} - r_{k}^{t - 2}} )}} \\{\Phi_{k}^{t} = {\Phi_{k}^{t - 1} + ( {\Phi_{k}^{t - 1} - \Phi_{k}^{t - 1}} )}}\end{matrix}\Rightarrow\{ \begin{matrix}{{\hat{r}}_{k}^{t} = {{2 \cdot r_{k}^{t - 1}} - r_{k}^{t - 2}}} \\{\Phi_{k}^{t} = {{2 \cdot \Phi_{k}^{t - 1}} - \Phi_{k}^{t - 2}}}\end{matrix}  } & (12)\end{matrix}$

Spectrum of three consecutive periods of ECG is shown in upper part ofFIG. 8. UM graph obtained from that spectrum and two previous sampleframes is shown in the bottom part of FIG. 8. Spectrum and UM,consequently, of the same signal but contaminated with artificiallyadded Gaussian white noise are presented in FIG. 9. It can be observedthat local maxima of the spectrum from FIG. 6 correspond with the localminima (low UM values) from the figure. While in noisy signal, presentedin FIG. 9, high magnitude level of the spectrum bins do not correspondwith low UM values in bottom part of the figure.

Since the sinusoidal components in a clean quasi-periodic signal usuallycarry the most energy of the signal and the UM values of such componentsare near zero, the UM weighted noise energy estimate for time t iscalculated in the following way:

$\begin{matrix}{E^{t} = {( {1 - \frac{\sum\limits_{k = 1}^{K}\;{( {1 - \alpha_{k}^{t}} ) \cdot {X_{k}^{t}}^{2}}}{\sum\limits_{k = 1}^{K}\;{X_{k}^{t}}^{2}}} ) \cdot {\sum\limits_{k = 1}^{K}\;{X_{k}^{t}}^{2}}}} & (13)\end{matrix}$where:X—ECG block spectrum,t—time instance,k—spectrum bin index,E—noise energy estimate.

An Auxiliary Information Calculation Algorithm (AICA) 370 is employed aswell. AICA module 370 is responsible for calculating average HR,irregularity indicator and HRV, ADC interference level, ST elevation, QTinterval and TWA amplitude.

The ST level, QT interval calculator analyze periods similarity and onlyfor properly classified complexes produce the ST deviation and QTinterval results, based on an averaged ECG period. The similaritycalculation utilizes smoothed ECG (v1 from Eq. 4) and it can beexpressed as:

$\begin{matrix}{S = \frac{\sum\limits_{k = 1}^{K}{{{p_{1}\lbrack k\rbrack} - {p_{2}\lbrack k\rbrack}}}^{2}}{\sum\limits_{k = 1}^{K}{p_{1}\lbrack k\rbrack}^{2}}} & \lbrack 14\rbrack\end{matrix}$where:S—similarity,p1, p2—ECG periods,K—ECG period size.

If S is below predefined similarity threshold, than p1, p2 are used forupdating the averaged ECG period. The averaged period is updated in arecursive way:

$\begin{matrix}{P_{avg} = \frac{{P_{avg} \cdot ( {K - 1} )} + p_{new}}{K}} & (15)\end{matrix}$where:Pavg—averaged ECG period,pnew—new ECG period (for S from Eq. 14 below predefined threshold),K—averaging order.

Using averaged ECG period for ST segment elevation, QT intervalcalculations allows for decreasing noise influence and results in robustT wave analysis. Averaged ECG period (black dotted line) vs periods usedfor calculating the Pavg (gray solid lines) are presented in FIG. 10.

Many of the QT interval measurement algorithms detect T wave end and thebeginning of isoelectric baseline for each ECG period. Since thebaseline is often disturbed with parasite noise, low frequencydisturbances of non-stationary character, these methods assume highquality of the analyzed signal fragment, hence can be utilized only inresting ECG. Many studies show that multi-channel (preferably 12 lead)ECG has to be utilized in order to optimally determine the T wave end.The presented approach is indirect and requires only singledetermination of QT interval at the beginning of the monitoring session,which can be done manually with the use of 12 lead ECG. The initial QTinterval determination also results in extracting reference ECG periodand reference T wave, which will be utilized for estimating QT intervalsof each non-pathological beat. In fact, the algorithm calculates QTinterval variability with regard to the reference T wave and since thereference QT interval is known, all other QT intervals can bedetermined. The time domain T wave shift is calculated with the use ofmodified AMDF (Average Magnitude Difference Function) technique, i.e.,it is in fact inverted and normalized ASDF (Average Squared DifferenceFunction). Both are popular methods used in speech processing.

Unlike the method which utilizes the time altered (stretched) timedomain representation of the T wave, the invented algorithm utilizestime domain shifted difference signal of the T wave. Such an approach ismore suitable, because the stretching (deformation) factor of the T waveis unknown, and may vary for different individuals' ECG resulting ininappropriate matching of the T wave decaying slope (i.e., T wave end).Since the attack phase of the T wave (i.e., energy increase at thebeginning of the T wave) is less rapid than the energy decay of the Twave, therefore the difference signal around the decaying slope has thehighest energy, hence the matching function has the maximum value forthe time domain shift matching the decaying slopes. Using the differencesignal also eliminates baseline level influence and the influence mayaffects similarity calculations carried out by the matching function,when processing not filtered ECGs. It must be stressed that standard(IIR or FIR) high pass filtering should not be applied here for thebaseline elimination due to influence of the so called Gibbs effect(oscillatory deformations) on the baseline segment which may occur afterthe T wave end, and may result in erroneous QT interval variabilitycalculations. The T wave matching function of the invented algorithm canbe expressed in the following way:

$\begin{matrix}{{p\lbrack n\rbrack} = {1 - \frac{\sum\limits_{k = {n - N + 1}}^{n}\;( {( {{s_{1}\lbrack k\rbrack} - {s_{1}\lbrack {k - 1} \rbrack}} ) - ( {{s_{2}\lbrack k\rbrack} - {s_{2}\lbrack {k - 1} \rbrack}} )} )^{2}}{\sum\limits_{1 = {N - n + 1}}^{{2 \cdot N} - n}\;\frac{( {{s_{1}\lbrack l\rbrack} - {s_{1}\lbrack {l - 1} \rbrack}} ) + ( {{s_{2}\lbrack l\rbrack} - {s_{2}\lbrack {l - 1} \rbrack}} )^{2}}{4}}}} & (16)\end{matrix}$where:P—matching curve of the time domain shifted T wave difference signals,N—number of samples of the compared T waves,n=1, . . . , 2·N−1s₁, s₂—compared T waves.

It must be stressed that the compared ECG periods are in factrecursively averaged (see Eq. 15) periods. Averaging allows fordecreasing influence of the parasite noise and parasite disturbances.Upper part of FIG. 11A presents the reference ECG period (solid line) vsnew period (solid/dotted line), while bottom part of the figure showsdifference signals of the T waves. T wave time shift is easilydistinguishable in the figure, however T wave end for the new periodwould be impossible to mark, due to the P wave close presence. Utilizingthe proposed method allows for prediction (based on the similarity ofthe reference T wave and the analyzed new T wave) of the QT interval.

FIG. 11B shows the similarity curve with regard to Eq. 16, presentingthe time shift influence of the compared T wave representations (i.e.,the T wave signals from the bottom part of FIG. 11A). Applying parabolicinterpolation on the similarity curve, i.e., fitting parabola to themaximum sample and the surrounding samples allows for achieving T waveshift accuracy significantly exceeding the ECG signal sampling ratelimitations:

$\begin{matrix}{\quad\{ {{\begin{matrix}{c = v_{\max}} \\{b = \frac{v_{R} - v_{L}}{2}} \\{a = {v_{L} + \frac{v_{R} - v_{L}}{2} - c}}\end{matrix}I_{dv}} = {{\frac{- b}{2 \cdot a}{\hat{i}}_{\max}} = {i_{\max} + I_{dv}}}} } & (17)\end{matrix}$where:v_(max)—value of the maximum sample of the similarity curve,v_(L), v_(R)—value of the left and right sample surrounding the maximum,I_(dv)—correction value of the maximum sample index,i_(max)—index of the maximum sample of the similarity curve,î_(max)—approximated index with the use of parabolic interpolation.

It is important to mention that the T wave shape may change over time.In most of the cases the reason for the shape evolution is changingheart rate, where the T wave amplitude and duration adopts to thecardiac cycle duration. Also, in case of significant ST segmentelevation episodes, or in case of T wave alternans episodes, or bodyposition changes, etc. the T wave shape may also evolve over time.Therefore, to efficiently deal with such situations and in order toavoid comparison of the reference T wave selected at the beginning ofthe examination, with the new T waves of significantly different shape,auxiliary T wave reference signals are automatically collected duringthe processing. When the reference T wave and the currently processednew T wave similarity value is below predefined threshold, and also ifthe new T wave and its N preceding T waves similarity value are abovethe predefined threshold, than the new processed T wave becomes a newauxiliary reference T wave. The similarity values are calculated in thefollowing way:

$\begin{matrix}{d_{sim} = {1 - \frac{\sum\limits_{n = 1}^{N}\;( {{s_{ref}\lbrack n\rbrack} - {s_{new}\lbrack n\rbrack}} )^{2}}{\sum\limits_{n = 1}^{N}\;\frac{( {{s_{ref}\lbrack n\rbrack} + {s_{new}\lbrack n\rbrack}} )^{2}}{4}}}} & (18)\end{matrix}$where:d_(sim)—similarity value of the compared T waves,N—number of samples of the compared T waves,s_(ref)—reference T wave signal,s_(new)—new T wave signal.

Thus, the initial reference T wave and the gathered auxiliary T wavesform a reference T wave set, consisting of K signals. For each new Twave and all K references d_(sim) ^(k) values are obtained:

$\begin{matrix}{d_{sim}^{k} = {1 - \frac{\sum\limits_{n = 1}^{N}\;( {{s_{ref}^{k}\lbrack n\rbrack} - {s_{new}\lbrack n\rbrack}} )^{2}}{\sum\limits_{n = 1}^{N}\;\frac{( {{s_{ref}^{k}\lbrack n\rbrack} + {s_{new}\lbrack n\rbrack}} )^{2}}{4}}}} & (19)\end{matrix}$

Maximum value of d_(sim) ^(k) is treated as the new T wave similarity tothe reference T wave signal and based on this value the decision ofupdating the reference T wave set is made.

The TWA amplitude calculation algorithm utilizes smoothed, but notaveraged ECG periods. Because of the instant character of the phenomenon(i.e., T wave amplitude fluctuates in every second beat) averaged ECGperiod is not directly used by the algorithm, but it is used forremoving slowly varying T wave shape trend. In the first step, however,it is necessary to remove baseline offset from the processed ECGperiods:b _(i)=median({p _(i) [k], . . . ,p _(i) [k+L]}), for kεK  (20)where:p_(i)—current (ith) ECG period,K—set of indices representing baseline,b_(i)—current (ith) base line level,

In addition, base line deformation (bd_(i)) is calculated. bd_(i) is astandard deviation of the base line difference signal of the current andthe previous smoothed ECG period:

$\begin{matrix}{{{bd}_{i} =},{\frac{1}{{K} - 1}\sqrt{\sum\limits_{k \in K}^{\;}\;\lbrack {( {{p_{i}\lbrack k\rbrack} - {p_{i - 1}\lbrack k\rbrack}} ) - {\frac{1}{K}{\sum\limits_{k \in K}^{\;}\;( {{p_{i}\lbrack k\rbrack} - {p_{i - 1}\lbrack k\rbrack}} )}}} \rbrack^{2}}}} & (21)\end{matrix}$where:bd_(i)—current (ith) ECG period baseline deformation,K—set of indices representing baseline,|K|—the baseline indices set strength, i.e., the number of the indices.

As mentioned before, the T wave shape may evolve, i.e., it may containan additive low frequency component related to e.g., ST episodestransient, as presented in FIG. 11C. It can be observed in FIG. 11C,that even periods (solid line) group and odd periods (dashed line) groupare not separated. The T wave shape may slowly vary and at the same timeTWA might be present. In such situations, analyzing averaged or medianeven beats versus averaged or median odd beats approach [8] may causeproblems. For example, the standard deviations of the two (even and odd)sets may overlap, making the two groups difficult to separate. Thereforethe invented algorithm does not separate even and odd ECG periods in twosets, it but analyzes periodicity of the TWA fluctuations, afterremoving the shape varying trend.

The low frequency trend is removed by subtracting the averaged ECGperiod from the current ECG period:

$\begin{matrix}{{{{{\overset{\_}{p}}_{i}\lbrack n\rbrack} = {( {{p_{i}\lbrack n\rbrack} - b_{i}} ) - ( {{\frac{1}{K}{\sum\limits_{k = {i - K + 1}}^{i}\;{p_{k}\lbrack n\rbrack}}} - b_{k}} )}};}{{{{for}\mspace{14mu} n} = 1},\ldots\mspace{14mu},N}} & (22)\end{matrix}$where:N—number of samples of the processed ECG periods,p—current ECG period,p—unbiased, current ECG period,M—the number of averaged ECG periods,b—base line level.

Six (6) consecutive, unbiased ECG periods, i.e., processed ECG periodsfrom FIG. 11C are shown in FIG. 11D. It can be observed in the figure,that even unbiased periods (solid line) group and odd unbiased periods(dashed line) group are well separated.

In the next step, periodicity value describing each sample of thecurrent ECG period, based on L preceding ECG periods is calculated. Theperiodicity character is calculated based on N autocorrelationsequences.

$\begin{matrix}{{{\hat{R}}_{n}\lbrack i\rbrack} = \{ \begin{matrix}{{\sum\limits_{j = 1}^{J - i}\;{{p_{n}\lbrack {j + i} \rbrack} \cdot {p_{n}^{*}\lbrack j\rbrack}}},{m \geq 0}} \\{{{\hat{R}}_{n}^{*}\lbrack {- i} \rbrack},{m < 0}}\end{matrix} } & ( {23a} ) \\{v_{n}^{1} = {1 - \frac{\sum\limits_{j = 1}^{J/2}\;( {{{\hat{R}}_{n}\lbrack {{j \cdot 2} - 1} \rbrack} + {{\hat{R}}_{n}\lbrack {j \cdot 2} \rbrack}} )^{2}}{\sum\limits_{j = 1}^{J/2}\;( {{{\hat{R}}_{n}\lbrack {{j \cdot 2} - 1} \rbrack} - {{\hat{R}}_{n}\lbrack {j \cdot 2} \rbrack}} )^{2}}}} & ( {23b} ) \\{v_{n}^{2} = {\frac{2}{{J/2} - 1}{\sum\limits_{j = 1}^{{J/2} - 1}\;\frac{{\hat{R}}_{n}\lbrack {{j \cdot 2} + {J/2} - 2} \rbrack}{{\hat{R}}_{n}\lbrack J\rbrack}}}} & ( {23c} ) \\{P_{n} = {v_{n}^{1} + v_{n}^{2}}} & ( {23d} )\end{matrix}$where:{circumflex over (R)}_(n)—nth autocorrelation sequenceJ—the number of periods used for calculating the autocorrelationsequences,n—ECG period index number,P_(n)—periodicity value.

The unbiased-averaged-difference-ECG-period signal is calculated asfollows:

$\begin{matrix}{{\hat{p}}_{i} = {{\sum\limits_{j = {j - {J/2}}}^{i}\;{\overset{\_}{p}}_{j \cdot 2}} - {\overset{\_}{p}}_{{j \cdot 2} - 1}}} & (24)\end{matrix}$where:{circumflex over (p)}_(i)—current (ith)unbiased-averaged-difference-ECG-period,J—the number of periods used for periodicity analysis and for averaging.

The periodicity curve is presented in FIG. 11E (upper part).Unbiased-averaged-difference-ECG-period is presented in the bottom partof FIG. 11E.

Finally, the T wave alternans amplitude of the current (ith) ECG periodis the maximum value, obtained for the T wave region in theunbiased-averaged-difference-ECG-period weighted by the periodicityvalue, and compensated by the base line fluctuations correction value(Eq. 20) and the baseline deformation correction value (Eq. 21):

$\begin{matrix}{{p_{prdc}^{i}\lbrack n\rbrack} = {{{\hat{p}}_{i}\lbrack n\rbrack} \cdot P_{n}}} & ( {25a} ) \\{{{bc}_{i} =},{\frac{1}{J - 1}\sqrt{\sum\limits_{j = {i - J + 1}}^{i}\;\lbrack {b_{j} - {\frac{1}{J}{\sum\limits_{k = {i - J + 1}}^{k}\; b_{k}}}} \rbrack^{2}}}} & ( {25b} ) \\{A_{TWA}^{i} = {{\max\limits_{n \in N^{T}}( p_{prdc}^{i} )} - ( {{bc}_{i} + {bd}_{i}} )}} & ( {25c} )\end{matrix}$where:p_(prdc) ^(i)—current (ith) unbiased-averaged-difference-ECG-periodweighted by the periodicity ECG period representation,bc_(i)—base line deviation,N^(T)—set of indices related to the T wave region,A_(TWA) ^(i)—current (calculated for the ith ECG period) T wavealternans level.

The algorithm calculates T wave alternans amplitude on the beat-to-beatbasis. The method provides very robust results and is resistant toparasite broad band noise and artifacts, because this kind ofdisturbances affect the periodicity character of the T wave shapefluctuations, hence the periodicity analysis (Eq. 23a-23d) eliminatespotentially incorrectly detected T wave amplitude variations. Inaddition, potentially incorrectly generated T wave level fluctuationsproduced by parasite base line level drift are compensated by the baseline level standard deviation value (Eq. 25b) and the base linedeformation value (Eq. 21).

The system 100 (FIG. 1) has ability to adopt to analyzed signal and dueto learning procedures and deals effectively with various and differentECG signals. The method operates in real-time and returns informationabout the detected ECG events.

The T wave related parameters, i.e., ST segment elevation, QT intervalduration and TWA amplitude can be calculated only in case of properlydetected QRS complexes and correctly classified beats, therefore the QRSdetection and classification component and the arrhythmia detectioncomponent of the system is of great importance and is the basis forproper T wave analysis. Many arrhythmia recognition, peak classificationand auxiliary information algorithm blocks operate on predefined, oradaptively calculated parameters. The above-described systems andmethods can be implemented in digital electronic circuitry, in computerhardware, firmware, and/or software. The implementation can be as acomputer program product (i.e., a computer program tangibly embodied inan information carrier). The implementation can, for example, be in amachine-readable storage device and/or in a propagated signal, forexecution by, or to control the operation of, data processing apparatus.The implementation can, for example, be a programmable processor, acomputer, and/or multiple computers.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions by operating on inputdata and generating output. Method steps can also be performed by and anapparatus can be implemented as special purpose logic circuitry. Thecircuitry can, for example, be a FPGA (field programmable gate array)and/or an ASIC (application-specific integrated circuit). Modules,subroutines, and software agents can refer to portions of the computerprogram, the processor, the special circuitry, software, and/or hardwarethat implements that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can include, can beoperatively coupled to receive data from and/or transfer data to one ormore mass storage devices for storing data (e.g., magnetic,magneto-optical disks, or optical disks).

Data transmission and instructions can also occur over a communicationsnetwork. Information carriers suitable for embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices. Theinformation carriers can, for example, be EPROM, EEPROM, flash memorydevices, magnetic disks, internal hard disks, removable disks,magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor andthe memory can be supplemented by, and/or incorporated in specialpurpose logic circuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer having a display device. The displaydevice can, for example, be a cathode ray tube (CRT) and/or a liquidcrystal display (LCD) monitor. The interaction with a user can, forexample, be a display of information to the user and a keyboard and apointing device (e.g., a mouse or a trackball) by which the user canprovide input to the computer (e.g., interact with a user interfaceelement). Other kinds of devices can be used to provide for interactionwith a user. Other devices can, for example, be feedback provided to theuser in any form of sensory feedback (e.g., visual feedback, auditoryfeedback, or tactile feedback). Input from the user can, for example, bereceived in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

Packet-based networks can include, for example, the Internet, a carrierinternet protocol (IP) network (e.g., local area network (LAN), widearea network (WAN), campus area network (CAN), metropolitan area network(MAN), home area network (HAN)), a private IP network, an IP privatebranch exchange (IPBX), a wireless network (e.g., radio access network(RAN), 802.11 network, 802.16 network, general packet radio service(GPRS) network, HiperLAN), and/or other packet-based networks.Circuit-based networks can include, for example, the public switchedtelephone network (PSTN), a private branch exchange (PBX), a wirelessnetwork (e.g., RAN, bluetooth, code-division multiple access (CDMA)network, time division multiple access (TDMA) network, global system formobile communications (GSM) network), and/or other circuit-basednetworks.

The transmitting device can include, for example, a computer, a computerwith a browser device, a telephone, an IP phone, a mobile device (e.g.,cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The browser device includes, for example, a computer (e.g., desktopcomputer, laptop computer) with a world wide web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). The mobilecomputing device includes, for example, a Blackberry®.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the embodiments may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theembodiments described herein. Scope of the embodiments is thus indicatedby the appended claims, rather than by the foregoing description, andall changes that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

What is claimed is:
 1. A system for remote monitoring of a patient'sstate, the system comprising: a tele-rehabilitation or continuousarrhythmia diagnostics module configured to acquire continuous real-timebeat-to-beat electrocardiogram (ECG) signals of the patient; aninteractive user interface configured to acquire patient subjectivesymptoms, the patient subjective symptom association with an ECGfragment of the continuous real-time beat-to-beat ECG signals; ananalysis module configured to automatically annotate the ECG signalswith information describing each beat represented by the ECG signals andautomatically detect a cardiac event, the analysis module furtherconfigured to correlate the ECG signals with the automatically annotatedECG signals, automatically detected cardiac event, and patientsubjective symptoms; a communication module configured to transmit thecontinuous real-time beat-to-beat ECG signals, annotations,automatically detected events, and patient subjective symptoms to acontrol module; the control module configured to visual represent thecontinuous real-time beat-to-beat ECG signals, annotations,automatically detected events, and patient subjective symptoms; and areporting module configured to report annotations, for every ECG beatand/or the automatically detected cardiac event, and/or noise level foreach ECG beat and ADC network interference for each ECG beat.
 2. Amethod for analyzing limited-lead electrocardiogram (ECG) systemsignals, comprising: recording ECG signals, by a recording module, usingat least one lead; performing cardiac tele-rehabilitation, by atele-rehabilitation module, wherein performing cardiactele-rehabilitation includes: recognizing erroneous data from the ECGsignals, wherein recognizing erroneous data from the ECG signalsincludes using a signal pre-processing algorithm (SPA) configured to i)divide the ECG signals into overlapping blocks, wherein block sizes arerelated to a calculated average hear rate (HR), ii) subtract, from eachblock, a mean value of each block, and iii) reconstruct the ECG signalsby cross-fading the successive segments; calculating annotations forevery ECG beat; reporting the calculated annotations, by a reportingmodule, for every ECG beat and/or the ECG events and/or noise level foreach ECG beat and ADC network interference for each ECG beat.
 3. Themethod of claim 2, wherein the ECG event is reported, by the reportingmodule, to medical personnel or a wearer of the recordation module. 4.The method of claim 2, wherein the calculated annotations representingeach ECG beat are segmented and then reported, by the reporting module,to medical personnel.
 5. The method of claim 2, further comprisingreceiving, via the reporting module, the annotations for each ECG beatand/or the ECG event at a remote location.
 6. The method of claim 2,wherein the erroneous data from the ECG signals is detected using ananalysis algorithm.
 7. The method of claim 6, wherein the analysisalgorithm includes a noise and distortion detection sub-algorithm (NDDA)for detecting noisy and non-linearly distorted ECG fragments and a ADCnetwork interference removing sub-algorithm (SPA).
 8. The method ofclaim 7, wherein the NDDA further estimates a broad band noise energylevel of the signal.
 9. The method of claim 2, wherein a pre-classifiedsignal is analyzed using a beat classification algorithm and/or anarrhythmia detection algorithm.
 10. The method of claim 9, wherein theanalyzed signal is verified using a detection evaluation correctionalgorithm.
 11. The method of claim 9, wherein the beat classificationalgorithm and the arrhythmia detection algorithm generate the calculatedannotations for every ECG beat.
 12. The method of claim 11, furthercomprising updating an averaged normal ECG period for each newnon-pathological ECG period based on the performed beat classificationand arrhythmia detection.
 13. The method of claim 12, wherein theaveraged ECG period is used for calculating ST segment elevation and forQT interval duration difference between the averaged ECG period and thereference ECG period.
 14. The method of claim 13, wherein the referenceECG period is an averaged ECG period with a predetermined QT interval,allowing for QT interval determination of each new averaged ECG periodbased on the predetermined interval value and the current QT intervaldifference value.
 15. The method of claim 14, wherein auxiliaryreference ECG periods are collected if a shape changing T wave occurs.16. The method of claim 15, wherein the averaged ECG period collected isused as an auxiliary reference ECG period.
 17. The method of claim 13,wherein the QT interval difference is obtained by finding a best matchof a time domain shifted T wave representation signal of the averagedECG period and the T wave representation signal of the reference ECGperiod.
 18. The method of claim 17, wherein the signal representationsare difference signals of the averaged ECG period and the reference ECGperiod.
 19. The method of claim 17, wherein the best match of the timedomain shifted T wave representation of the averaged ECG period and theT wave representation of the reference ECG period is the maximum valueof a shifted T wave matching function.
 20. The method of claim 19,wherein index of the maximum value of a similarity function isinterpolated to enhance the T wave shift accuracy.
 21. The method ofclaim 20, wherein the interpolation is performed by a parabola fittingto the maximum similarity value and a surrounding values of thesimilarity curve.
 22. The method of claim 19, wherein the maximumsimilarity value is the maximum of all maximum similarity values of thereference ECG period and all collected auxiliary reference ECG periodscompared with a new averaged ECG period.
 23. The method of claim 11,wherein a non-pathological ECG period is a current ECG period used forcalculating a T wave alternans amplitude.
 24. The method of claim 23,wherein a value of the base line level is a median value of anisoelectric line signal segment preceding a current ECG period.
 25. Themethod of claim 24, wherein the value of a base line level deviation isa standard deviation of the current base line level and a J precedingbase line level.
 26. The method of claim 23, wherein a value of theisoelectric line deformation of the current ECG period is a standarddeviation of a difference of the current isoelectric line, preceding thecurrent ECG period and an isoelectric line preceding the previous ECGperiod.
 27. The method of claim 23, wherein an unbiased current ECGperiod is calculated by removing a low frequency T wave shape trend fromthe current ECG period.
 28. The method of claim 27, wherein the lowfrequency T trend is removed by subtracting an averaged ECG period fromthe current ECG period.
 29. The method of claim 23, wherein aperiodicity values representing each sample of the current ECG periodare calculated.
 30. The method of claim 29, wherein the periodicityvalues for n samples of the current ECG period are calculated based on nautocorrelation sequences, calculated with the use of a J consecutiveunbiased ECG periods, including the current unbiased ECG period.
 31. Themethod of claim 30, wherein a T wave amplitude is calculated based on anunbiased-averaged-difference-ECG-period.
 32. The method of claim 31,wherein the unbiased-averaged-difference-ECG-period is calculated basedon J/2 pairs of a J consecutive unbiased ECG periods, the preceding thecurrent unbiased ECG period and including the current unbiased ECGperiod.
 33. The method of claim 32, wherein the T wave altemans is amaximum of the unbiased-averaged-difference-ECG-period.
 34. The methodof claim 33, wherein an unbiased-averaged-difference-ECG-period isweighted by the periodicity values.
 35. The method of claim 34, whereina maximum value of the unbiased-averaged-difference-ECG-period, weightedby periodicity values and compensated by base line drift deviationvalues and isoelectric line deformation values is a calculated T wavealtemans amplitude for the current ECG period.